How many datasets are required in a prediction problem?

Question

When working with prediction problems, is there a need to consider the change in time or not?

For example, when trying to predict the price of a house, we just have the current features and the current price. The classification problem is to predict if the house price will be above a threshold or not. The regression problem is to predict the exact price.

Is there a need to have datasets with some time interval between them (for example X days or Y years) or we can predict with a dataset that was taken at a certain time? And in this case, is there a difference between classification and regression?

Answer 1

Is there a need to have datasets with some time interval between them (for example X days or Y years) or we can predict with a dataset that was taken at a certain time?

If you have some data that displays pattern of changing prices (or your target) over time, then sure, you have to consider the time change. This data will then become a time-series data, which has to be processed differently than normal data.

And in this case, is there a difference between classification and regression?

The problem of predicting prices at first-hand is regression, because the prices take continuous values. But you can convert it to classes according to your threshold. So in this case, (as far as I understand), there is no difference between regression and classification, as you are just putting numbers (prices) in different classes.

Hope it helps.

Please comment if something is not clear.